SYNCS: Synthetic Data and Contrastive Self-Supervised Training for Central Sulcus Segmentation
This work addresses a domain-specific problem in neuroimaging for early disease marker identification in mental disorders, representing an incremental improvement in segmentation methods.
This study tackled the challenge of segmenting the central sulcus in brain MRI scans, especially for adolescents with familial high risk of bipolar disorder or schizophrenia, by introducing synthetic data generation and self-supervised pre-training with multi-task learning to improve segmentation performance across diverse populations.
Bipolar disorder (BD) and schizophrenia (SZ) are severe mental disorders with profound societal impact. Identifying risk markers early is crucial for understanding disease progression and enabling preventive measures. The Danish High Risk and Resilience Study (VIA) focuses on understanding early disease processes, particularly in children with familial high risk (FHR). Understanding structural brain changes associated with these diseases during early stages is essential for effective interventions. The central sulcus (CS) is a prominent brain landmark related to brain regions involved in motor and sensory processing. Analyzing CS morphology can provide valuable insights into neurodevelopmental abnormalities in the FHR group. However, segmenting the central sulcus (CS) presents challenges due to its variability, especially in adolescents. This study introduces two novel approaches to improve CS segmentation: synthetic data generation to model CS variability and self-supervised pre-training with multi-task learning to adapt models to new cohorts. These methods aim to enhance segmentation performance across diverse populations, eliminating the need for extensive preprocessing.